用普通人类动作生成逼真四足动物动画，支持精细控制。
Two2Four: Generative Quadruped Puppeteering from Human Motion
- 两阶段扩散模型从人类动作生成四足动物运动
- 可生成行走、奔跑、跳跃等多样动作，真实感更强
- 支持头部和单肢的精细操控，适合影视动画制作
虚拟制作中，逼真的动物运动通常依赖训练有素的表演者进行动作捕捉，或通过复杂控制结构将普通人动作重定向。这两种方法都面临挑战，难以完全还原自然动物动作的细节。本文提出一种自动的人类到四足动物的操纵框架，仅使用四足动物动作数据训练的两阶段生成扩散模型，实现从普通人类动作数据生成合理且可控的四足动物运动。通过引入结构化条件与图像修复策略，该方法支持多种动作，包括行走、奔跑、跳跃、坐下和躺卧。此外，还实现了对头部运动和各肢体的细粒度直观控制。实验表明，相比现有重定向方法，本方法在运动真实性和可控性上均有提升，验证了其在动画与虚拟制作中的有效性。
原文摘要 · Abstract (English)
Realistic animal motion for virtual production is typically obtained either through motion capture of highly trained performers who accurately mimic animal behavior, or by retargeting ordinary human motion using complex control setups. Both approaches are challenging and often fail to fully reproduce the nuances of natural animal motion, motivating data-driven alternatives. We present an automatic human-to-quadruped puppeteering framework that produces plausible and controllable quadruped motions from ordinary human motion data. Our approach employs a two-stage generative diffusion model trained purely on quadruped motion data. By introducing a structured conditioning and inpainting strategy, our method supports a wide range of actions, including walking, running, jumping, sitting, and lying. Furthermore, we enable fine-grained intuitive control of the quadruped motion such as head movement control and individual limb puppeteering. Experimental results demonstrate improved motion realism and controllability compared to existing retargeting approaches, highlighting the effectiveness of our framework as a tool for animation and virtual production applications.
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